Reduced-Item Food Audits Based on the Nutrition Environment Measures Surveys

Am J Prev Med. 2015 Oct;49(4):e23-33. doi: 10.1016/j.amepre.2015.04.036. Epub 2015 Jul 21.


Introduction: The community food environment may contribute to obesity by influencing food choice. Store and restaurant audits are increasingly common methods for assessing food environments, but are time consuming and costly. A valid, reliable brief measurement tool is needed. The purpose of this study was to develop and validate reduced-item food environment audit tools for stores and restaurants.

Methods: Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed in 820 stores and 1,795 restaurants in West Virginia, San Diego, and Seattle. Data mining techniques (correlation-based feature selection and linear regression) were used to identify survey items highly correlated to total survey scores and produce reduced-item audit tools that were subsequently validated against full NEMS surveys. Regression coefficients were used as weights that were applied to reduced-item tool items to generate comparable scores to full NEMS surveys. Data were collected and analyzed in 2008-2013.

Results: The reduced-item tools included eight items for grocery, ten for convenience, seven for variety, and five for other stores; and 16 items for sit-down, 14 for fast casual, 19 for fast food, and 13 for specialty restaurants-10% of the full NEMS-S and 25% of the full NEMS-R. There were no significant differences in median scores for varying types of retail food outlets when compared to the full survey scores. Median in-store audit time was reduced 25%-50%.

Conclusions: Reduced-item audit tools can reduce the burden and complexity of large-scale or repeated assessments of the retail food environment without compromising measurement quality.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • California
  • Cities / statistics & numerical data
  • Environment
  • Food / statistics & numerical data*
  • Food Supply / statistics & numerical data*
  • Machine Learning
  • Nutrition Surveys*
  • Residence Characteristics / statistics & numerical data*
  • Restaurants / statistics & numerical data*
  • Washington
  • West Virginia